Adaptation of the Submerged Pumps Intended for the Irrigation in the Arid Regions
CHEBIHI Lakhdar,
KHODJET KESBA Omar
Issue:
Volume 3, Issue 2, April 2014
Pages:
17-21
Received:
28 April 2014
Accepted:
21 May 2014
Published:
30 May 2014
Abstract: The first area of work is to study only submersible pumps PUVAL "Pumps Valves" of Berrouaghia. While tracing the effects of abrasion on submersible pumps installed in the shelters in drilling and suffered the consequences are for irrigation in arid areas. The second line stain work to answer the following position: "Should we fight against the causes and not against the consequences?" The result of the proposals will help manufacturers to pump Algerian, one with the best hydraulic performance for the chosen material. Certainly, there have been abrasion tests on samples, but determining the duration of wear and the wear rate of the types of materials is always a line of news. Increasing the life of the pump while remaining within the proper range of operation.
Abstract: The first area of work is to study only submersible pumps PUVAL "Pumps Valves" of Berrouaghia. While tracing the effects of abrasion on submersible pumps installed in the shelters in drilling and suffered the consequences are for irrigation in arid areas. The second line stain work to answer the following position: "Should we fight against the caus...
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Monthly Predicted Flow Values of the Sanaga River in Cameroon Using Neural Networks Applied to GLDAS, MERRA and GPCP Data
SIDDI Tengeleng,
NZEUKOU Armand,
KAPTUE Armel,
TCHAKOUTIO SANDJON Alain,
SIMO Théophile,
Djiongo Cedrigue
Issue:
Volume 3, Issue 2, April 2014
Pages:
22-29
Received:
23 April 2014
Accepted:
23 May 2014
Published:
30 May 2014
Abstract: The aim of our study is to predict the discharge rate of the river Sanaga using neural network techniques. Our investigations have taken place in the Sanaga watershed area in Cameroon. The measurement station is situated in the locality of Edea-Song-Mbengue (04°04’15”N, 10°27’50”E) where we have obtained monthly values of the river Sanaga discharge rates that have been measured in situ from January 1989 to December 2004. We have trained neural networks (NN), each with data of parameters such as the surface albedo, the total cloud fraction, the evaporation, the outgoing longwave radiation, the air temperature, the specific humidity, the surface runoff and the precipitation height. The precipitation values have been obtained from GPCP (Global Precipitation Climatology Project) and those of the other parameters from the data assimilation systems GLDAS (Global Land Data Assimilation System) and MERRA (Modern Era-Retrospective analysis for Research and Application). As desired outputs of the NN during the learning process, we have used the measured river runoff values. After introducing temporal delays of 01 and 02 months in the learning-process, we could observe the presence of the memory effect of the parameters used on the temporal evolution of the river discharge rate. After analysis of the performance's criteria of the NN with the help of the calculated Root Means Square Errors (RMSE) and determination coefficients between predicted values and in situ observed ones, we have perceived that the NN which takes into account the two-month delay can predict the river discharge rate with a strong correlation.
Abstract: The aim of our study is to predict the discharge rate of the river Sanaga using neural network techniques. Our investigations have taken place in the Sanaga watershed area in Cameroon. The measurement station is situated in the locality of Edea-Song-Mbengue (04°04’15”N, 10°27’50”E) where we have obtained monthly values of the river Sanaga discharge...
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